The trigger point for that is the moment — after you have enough clinical data collected, and submitted to the FDA and gotten it approved — when it can be reimbursed under an existing payer code.
These features were weight (97% values missing), payer code (40%), and medical specialty (47%).
Payer code was removed since it had a high percentage of missing values and it was not considered relevant to the outcome.
According to the principal payer code in the database, elevated AP antibody titer patients were less likely to be Medicaid beneficiaries than patients free of elevated AP antibody titers.
Because of variations by state in coding procedures, we excluded records with either primary or secondary payers coded as "no charge" (approximately 0.3% of all records in the database) or "other" (3.2% of records in the database).
Hospital market areas and the HHI were calculated based on all payer zip code level patient flows, as described in Zwanziger et al. [ 11].
For the purposes of the models, the variable payer was coded such that a response of uninsured was utilized as the reference group and compared to the privately insured (i.e., insurance provided by one's employer), government insurance (i.e., Medicaid, Medicare, Veterans Benefits Administration, Indian Health Services), and payment (other) cohorts.
While some studies investigated organizational behavior and reported potential influence of hospital management and payer on coding process [ 19, 20], other studies tried to demonstrate potential upcoding of some specific diagnoses such as pneumonia and heart failure [ 21, 22].
Records in these data files include a facility identifier indicating where the hospitalization occurred, patient demographics, characteristics of the admission, principal and secondary International Classification of Diseases (ICD) diagnosis and procedure codes, payer, length of stay, discharge status (alive/dead) and destination, and Diagnosis Related Groups (DRG) assignment.
Visit-level data elements within the PCDP included patient demographic characteristics (e.g., age, gender, race, payer), ICD-9-CM codes and E-codes, and outcome of the visit (admitted, transferred, ED death, or discharge/other).
Parameters assessed included knowledge and use of existing MNT and/or alternative procedure codes, barriers to code use, payer reimbursement patterns, complexity of the patient population served, time spent in the delivery of initial and subsequent care, and practice demographics and management.
